{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('GlobalLandTemperaturesByCountry.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1743-11-01</td>\n",
       "      <td>4.384</td>\n",
       "      <td>2.294</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1743-12-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1744-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1744-02-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1744-03-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1744-04-01</td>\n",
       "      <td>1.530</td>\n",
       "      <td>4.680</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1744-05-01</td>\n",
       "      <td>6.702</td>\n",
       "      <td>1.789</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1744-06-01</td>\n",
       "      <td>11.609</td>\n",
       "      <td>1.577</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1744-07-01</td>\n",
       "      <td>15.342</td>\n",
       "      <td>1.410</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1744-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Åland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dt  AverageTemperature  AverageTemperatureUncertainty Country\n",
       "0  1743-11-01               4.384                          2.294   Åland\n",
       "1  1743-12-01                 NaN                            NaN   Åland\n",
       "2  1744-01-01                 NaN                            NaN   Åland\n",
       "3  1744-02-01                 NaN                            NaN   Åland\n",
       "4  1744-03-01                 NaN                            NaN   Åland\n",
       "5  1744-04-01               1.530                          4.680   Åland\n",
       "6  1744-05-01               6.702                          1.789   Åland\n",
       "7  1744-06-01              11.609                          1.577   Åland\n",
       "8  1744-07-01              15.342                          1.410   Åland\n",
       "9  1744-08-01                 NaN                            NaN   Åland"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>544811.000000</td>\n",
       "      <td>545550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.193354</td>\n",
       "      <td>1.019057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.953966</td>\n",
       "      <td>1.201930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-37.658000</td>\n",
       "      <td>0.052000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.842000</td>\n",
       "      <td>15.003000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AverageTemperature  AverageTemperatureUncertainty\n",
       "count       544811.000000                  545550.000000\n",
       "mean            17.193354                       1.019057\n",
       "std             10.953966                       1.201930\n",
       "min            -37.658000                       0.052000\n",
       "25%                   NaN                            NaN\n",
       "50%                   NaN                            NaN\n",
       "75%                   NaN                            NaN\n",
       "max             38.842000                      15.003000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_country = df.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_Germany = df.drop('AverageTemperatureUncertainty', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_Germany = df_Germany[df_Germany.Country == \"Germany\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>204672</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>0.394</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204673</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>8.213</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204674</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>12.151</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204675</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>15.927</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204676</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>19.762</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204677</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>18.233</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204678</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3239 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dt  AverageTemperature  Country\n",
       "201440  1743-11-01               5.468  Germany\n",
       "201441  1743-12-01                 NaN  Germany\n",
       "201442  1744-01-01                 NaN  Germany\n",
       "201443  1744-02-01                 NaN  Germany\n",
       "201444  1744-03-01                 NaN  Germany\n",
       "201445  1744-04-01               8.438  Germany\n",
       "201446  1744-05-01              11.498  Germany\n",
       "201447  1744-06-01              14.262  Germany\n",
       "201448  1744-07-01              16.293  Germany\n",
       "201449  1744-08-01                 NaN  Germany\n",
       "201450  1744-09-01              13.050  Germany\n",
       "201451  1744-10-01               8.564  Germany\n",
       "201452  1744-11-01               4.164  Germany\n",
       "201453  1744-12-01               0.064  Germany\n",
       "201454  1745-01-01              -2.466  Germany\n",
       "201455  1745-02-01              -1.641  Germany\n",
       "201456  1745-03-01               1.968  Germany\n",
       "201457  1745-04-01               6.816  Germany\n",
       "201458  1745-05-01                 NaN  Germany\n",
       "201459  1745-06-01                 NaN  Germany\n",
       "201460  1745-07-01                 NaN  Germany\n",
       "201461  1745-08-01                 NaN  Germany\n",
       "201462  1745-09-01                 NaN  Germany\n",
       "201463  1745-10-01                 NaN  Germany\n",
       "201464  1745-11-01                 NaN  Germany\n",
       "201465  1745-12-01                 NaN  Germany\n",
       "201466  1746-01-01                 NaN  Germany\n",
       "201467  1746-02-01                 NaN  Germany\n",
       "201468  1746-03-01                 NaN  Germany\n",
       "201469  1746-04-01                 NaN  Germany\n",
       "...            ...                 ...      ...\n",
       "204649  2011-04-01              11.706  Germany\n",
       "204650  2011-05-01              14.242  Germany\n",
       "204651  2011-06-01              16.977  Germany\n",
       "204652  2011-07-01              16.538  Germany\n",
       "204653  2011-08-01              18.152  Germany\n",
       "204654  2011-09-01              15.605  Germany\n",
       "204655  2011-10-01               9.498  Germany\n",
       "204656  2011-11-01               4.611  Germany\n",
       "204657  2011-12-01               3.604  Germany\n",
       "204658  2012-01-01               1.753  Germany\n",
       "204659  2012-02-01              -2.467  Germany\n",
       "204660  2012-03-01               7.115  Germany\n",
       "204661  2012-04-01               8.259  Germany\n",
       "204662  2012-05-01              14.474  Germany\n",
       "204663  2012-06-01              15.623  Germany\n",
       "204664  2012-07-01              17.834  Germany\n",
       "204665  2012-08-01              18.822  Germany\n",
       "204666  2012-09-01              14.030  Germany\n",
       "204667  2012-10-01               8.847  Germany\n",
       "204668  2012-11-01               5.220  Germany\n",
       "204669  2012-12-01               1.216  Germany\n",
       "204670  2013-01-01              -0.067  Germany\n",
       "204671  2013-02-01              -0.731  Germany\n",
       "204672  2013-03-01               0.394  Germany\n",
       "204673  2013-04-01               8.213  Germany\n",
       "204674  2013-05-01              12.151  Germany\n",
       "204675  2013-06-01              15.927  Germany\n",
       "204676  2013-07-01              19.762  Germany\n",
       "204677  2013-08-01              18.233  Germany\n",
       "204678  2013-09-01                 NaN  Germany\n",
       "\n",
       "[3239 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_Germany = df_Germany.drop('Country',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201440</th>\n",
       "      <td>1743-11-01</td>\n",
       "      <td>5.468</td>\n",
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       "    <tr>\n",
       "      <th>201441</th>\n",
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       "    <tr>\n",
       "      <th>201444</th>\n",
       "      <td>1744-03-01</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>201445</th>\n",
       "      <td>1744-04-01</td>\n",
       "      <td>8.438</td>\n",
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       "    <tr>\n",
       "      <th>201446</th>\n",
       "      <td>1744-05-01</td>\n",
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       "      <th>201447</th>\n",
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       "    <tr>\n",
       "      <th>201448</th>\n",
       "      <td>1744-07-01</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>201449</th>\n",
       "      <td>1744-08-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201450</th>\n",
       "      <td>1744-09-01</td>\n",
       "      <td>13.050</td>\n",
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       "    <tr>\n",
       "      <th>201451</th>\n",
       "      <td>1744-10-01</td>\n",
       "      <td>8.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201452</th>\n",
       "      <td>1744-11-01</td>\n",
       "      <td>4.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201453</th>\n",
       "      <td>1744-12-01</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201454</th>\n",
       "      <td>1745-01-01</td>\n",
       "      <td>-2.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201455</th>\n",
       "      <td>1745-02-01</td>\n",
       "      <td>-1.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201456</th>\n",
       "      <td>1745-03-01</td>\n",
       "      <td>1.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201457</th>\n",
       "      <td>1745-04-01</td>\n",
       "      <td>6.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201458</th>\n",
       "      <td>1745-05-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201459</th>\n",
       "      <td>1745-06-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201460</th>\n",
       "      <td>1745-07-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201461</th>\n",
       "      <td>1745-08-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201462</th>\n",
       "      <td>1745-09-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201463</th>\n",
       "      <td>1745-10-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201464</th>\n",
       "      <td>1745-11-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201465</th>\n",
       "      <td>1745-12-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201466</th>\n",
       "      <td>1746-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201467</th>\n",
       "      <td>1746-02-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201468</th>\n",
       "      <td>1746-03-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201469</th>\n",
       "      <td>1746-04-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204649</th>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>11.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204650</th>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>14.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204651</th>\n",
       "      <td>2011-06-01</td>\n",
       "      <td>16.977</td>\n",
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       "    <tr>\n",
       "      <th>204652</th>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>16.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204653</th>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>18.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204654</th>\n",
       "      <td>2011-09-01</td>\n",
       "      <td>15.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204655</th>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>9.498</td>\n",
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       "    <tr>\n",
       "      <th>204656</th>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>4.611</td>\n",
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       "      <th>204657</th>\n",
       "      <td>2011-12-01</td>\n",
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       "      <th>204658</th>\n",
       "      <td>2012-01-01</td>\n",
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       "      <th>204659</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>-2.467</td>\n",
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       "    <tr>\n",
       "      <th>204660</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>7.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204661</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>8.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204662</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>14.474</td>\n",
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       "    <tr>\n",
       "      <th>204663</th>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>15.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204664</th>\n",
       "      <td>2012-07-01</td>\n",
       "      <td>17.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204665</th>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>18.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204666</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>14.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204667</th>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>8.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204668</th>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>5.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204669</th>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>1.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204670</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>-0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204671</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>-0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204672</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>0.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204673</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>8.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204674</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>12.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204675</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>15.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204676</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>19.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204677</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>18.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204678</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3239 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dt  AverageTemperature\n",
       "201440  1743-11-01               5.468\n",
       "201441  1743-12-01                 NaN\n",
       "201442  1744-01-01                 NaN\n",
       "201443  1744-02-01                 NaN\n",
       "201444  1744-03-01                 NaN\n",
       "201445  1744-04-01               8.438\n",
       "201446  1744-05-01              11.498\n",
       "201447  1744-06-01              14.262\n",
       "201448  1744-07-01              16.293\n",
       "201449  1744-08-01                 NaN\n",
       "201450  1744-09-01              13.050\n",
       "201451  1744-10-01               8.564\n",
       "201452  1744-11-01               4.164\n",
       "201453  1744-12-01               0.064\n",
       "201454  1745-01-01              -2.466\n",
       "201455  1745-02-01              -1.641\n",
       "201456  1745-03-01               1.968\n",
       "201457  1745-04-01               6.816\n",
       "201458  1745-05-01                 NaN\n",
       "201459  1745-06-01                 NaN\n",
       "201460  1745-07-01                 NaN\n",
       "201461  1745-08-01                 NaN\n",
       "201462  1745-09-01                 NaN\n",
       "201463  1745-10-01                 NaN\n",
       "201464  1745-11-01                 NaN\n",
       "201465  1745-12-01                 NaN\n",
       "201466  1746-01-01                 NaN\n",
       "201467  1746-02-01                 NaN\n",
       "201468  1746-03-01                 NaN\n",
       "201469  1746-04-01                 NaN\n",
       "...            ...                 ...\n",
       "204649  2011-04-01              11.706\n",
       "204650  2011-05-01              14.242\n",
       "204651  2011-06-01              16.977\n",
       "204652  2011-07-01              16.538\n",
       "204653  2011-08-01              18.152\n",
       "204654  2011-09-01              15.605\n",
       "204655  2011-10-01               9.498\n",
       "204656  2011-11-01               4.611\n",
       "204657  2011-12-01               3.604\n",
       "204658  2012-01-01               1.753\n",
       "204659  2012-02-01              -2.467\n",
       "204660  2012-03-01               7.115\n",
       "204661  2012-04-01               8.259\n",
       "204662  2012-05-01              14.474\n",
       "204663  2012-06-01              15.623\n",
       "204664  2012-07-01              17.834\n",
       "204665  2012-08-01              18.822\n",
       "204666  2012-09-01              14.030\n",
       "204667  2012-10-01               8.847\n",
       "204668  2012-11-01               5.220\n",
       "204669  2012-12-01               1.216\n",
       "204670  2013-01-01              -0.067\n",
       "204671  2013-02-01              -0.731\n",
       "204672  2013-03-01               0.394\n",
       "204673  2013-04-01               8.213\n",
       "204674  2013-05-01              12.151\n",
       "204675  2013-06-01              15.927\n",
       "204676  2013-07-01              19.762\n",
       "204677  2013-08-01              18.233\n",
       "204678  2013-09-01                 NaN\n",
       "\n",
       "[3239 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_Germany.index = pd.to_datetime(df_Germany.dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>AverageTemperature</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1743-11-01</th>\n",
       "      <td>1743-11-01</td>\n",
       "      <td>5.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743-12-01</th>\n",
       "      <td>1743-12-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-01-01</th>\n",
       "      <td>1744-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-02-01</th>\n",
       "      <td>1744-02-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-03-01</th>\n",
       "      <td>1744-03-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-04-01</th>\n",
       "      <td>1744-04-01</td>\n",
       "      <td>8.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-05-01</th>\n",
       "      <td>1744-05-01</td>\n",
       "      <td>11.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-06-01</th>\n",
       "      <td>1744-06-01</td>\n",
       "      <td>14.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-07-01</th>\n",
       "      <td>1744-07-01</td>\n",
       "      <td>16.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-08-01</th>\n",
       "      <td>1744-08-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-09-01</th>\n",
       "      <td>1744-09-01</td>\n",
       "      <td>13.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-10-01</th>\n",
       "      <td>1744-10-01</td>\n",
       "      <td>8.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-11-01</th>\n",
       "      <td>1744-11-01</td>\n",
       "      <td>4.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-12-01</th>\n",
       "      <td>1744-12-01</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-01-01</th>\n",
       "      <td>1745-01-01</td>\n",
       "      <td>-2.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-02-01</th>\n",
       "      <td>1745-02-01</td>\n",
       "      <td>-1.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-03-01</th>\n",
       "      <td>1745-03-01</td>\n",
       "      <td>1.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-04-01</th>\n",
       "      <td>1745-04-01</td>\n",
       "      <td>6.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-05-01</th>\n",
       "      <td>1745-05-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-06-01</th>\n",
       "      <td>1745-06-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-07-01</th>\n",
       "      <td>1745-07-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-08-01</th>\n",
       "      <td>1745-08-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-09-01</th>\n",
       "      <td>1745-09-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-10-01</th>\n",
       "      <td>1745-10-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-11-01</th>\n",
       "      <td>1745-11-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-12-01</th>\n",
       "      <td>1745-12-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-01-01</th>\n",
       "      <td>1746-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-02-01</th>\n",
       "      <td>1746-02-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-03-01</th>\n",
       "      <td>1746-03-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-04-01</th>\n",
       "      <td>1746-04-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>11.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>14.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>2011-06-01</td>\n",
       "      <td>16.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>16.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>18.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>2011-09-01</td>\n",
       "      <td>15.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>9.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>4.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>3.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1.753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>-2.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>7.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>8.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>14.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>15.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>2012-07-01</td>\n",
       "      <td>17.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>18.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>14.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>8.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>5.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>1.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>-0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>-0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>0.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>8.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>12.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>15.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>19.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>18.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3239 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dt  AverageTemperature\n",
       "dt                                        \n",
       "1743-11-01  1743-11-01               5.468\n",
       "1743-12-01  1743-12-01                 NaN\n",
       "1744-01-01  1744-01-01                 NaN\n",
       "1744-02-01  1744-02-01                 NaN\n",
       "1744-03-01  1744-03-01                 NaN\n",
       "1744-04-01  1744-04-01               8.438\n",
       "1744-05-01  1744-05-01              11.498\n",
       "1744-06-01  1744-06-01              14.262\n",
       "1744-07-01  1744-07-01              16.293\n",
       "1744-08-01  1744-08-01                 NaN\n",
       "1744-09-01  1744-09-01              13.050\n",
       "1744-10-01  1744-10-01               8.564\n",
       "1744-11-01  1744-11-01               4.164\n",
       "1744-12-01  1744-12-01               0.064\n",
       "1745-01-01  1745-01-01              -2.466\n",
       "1745-02-01  1745-02-01              -1.641\n",
       "1745-03-01  1745-03-01               1.968\n",
       "1745-04-01  1745-04-01               6.816\n",
       "1745-05-01  1745-05-01                 NaN\n",
       "1745-06-01  1745-06-01                 NaN\n",
       "1745-07-01  1745-07-01                 NaN\n",
       "1745-08-01  1745-08-01                 NaN\n",
       "1745-09-01  1745-09-01                 NaN\n",
       "1745-10-01  1745-10-01                 NaN\n",
       "1745-11-01  1745-11-01                 NaN\n",
       "1745-12-01  1745-12-01                 NaN\n",
       "1746-01-01  1746-01-01                 NaN\n",
       "1746-02-01  1746-02-01                 NaN\n",
       "1746-03-01  1746-03-01                 NaN\n",
       "1746-04-01  1746-04-01                 NaN\n",
       "...                ...                 ...\n",
       "2011-04-01  2011-04-01              11.706\n",
       "2011-05-01  2011-05-01              14.242\n",
       "2011-06-01  2011-06-01              16.977\n",
       "2011-07-01  2011-07-01              16.538\n",
       "2011-08-01  2011-08-01              18.152\n",
       "2011-09-01  2011-09-01              15.605\n",
       "2011-10-01  2011-10-01               9.498\n",
       "2011-11-01  2011-11-01               4.611\n",
       "2011-12-01  2011-12-01               3.604\n",
       "2012-01-01  2012-01-01               1.753\n",
       "2012-02-01  2012-02-01              -2.467\n",
       "2012-03-01  2012-03-01               7.115\n",
       "2012-04-01  2012-04-01               8.259\n",
       "2012-05-01  2012-05-01              14.474\n",
       "2012-06-01  2012-06-01              15.623\n",
       "2012-07-01  2012-07-01              17.834\n",
       "2012-08-01  2012-08-01              18.822\n",
       "2012-09-01  2012-09-01              14.030\n",
       "2012-10-01  2012-10-01               8.847\n",
       "2012-11-01  2012-11-01               5.220\n",
       "2012-12-01  2012-12-01               1.216\n",
       "2013-01-01  2013-01-01              -0.067\n",
       "2013-02-01  2013-02-01              -0.731\n",
       "2013-03-01  2013-03-01               0.394\n",
       "2013-04-01  2013-04-01               8.213\n",
       "2013-05-01  2013-05-01              12.151\n",
       "2013-06-01  2013-06-01              15.927\n",
       "2013-07-01  2013-07-01              19.762\n",
       "2013-08-01  2013-08-01              18.233\n",
       "2013-09-01  2013-09-01                 NaN\n",
       "\n",
       "[3239 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_Germany = df_Germany.drop('dt', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us take the data from 1970 onwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1743-11-01</th>\n",
       "      <td>5.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-02-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1744-03-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1744-04-01</th>\n",
       "      <td>8.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-05-01</th>\n",
       "      <td>11.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-06-01</th>\n",
       "      <td>14.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-07-01</th>\n",
       "      <td>16.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-09-01</th>\n",
       "      <td>13.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-10-01</th>\n",
       "      <td>8.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-11-01</th>\n",
       "      <td>4.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-12-01</th>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-01-01</th>\n",
       "      <td>-2.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-02-01</th>\n",
       "      <td>-1.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-03-01</th>\n",
       "      <td>1.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-04-01</th>\n",
       "      <td>6.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>11.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>14.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>16.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>16.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>18.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>15.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>9.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>4.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>3.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>1.753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>-2.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>7.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>8.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>14.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>15.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>17.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>18.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>14.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>8.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>5.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>1.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>-0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>0.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>8.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>12.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>15.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>19.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>18.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3239 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature\n",
       "dt                            \n",
       "1743-11-01               5.468\n",
       "1743-12-01                 NaN\n",
       "1744-01-01                 NaN\n",
       "1744-02-01                 NaN\n",
       "1744-03-01                 NaN\n",
       "1744-04-01               8.438\n",
       "1744-05-01              11.498\n",
       "1744-06-01              14.262\n",
       "1744-07-01              16.293\n",
       "1744-08-01                 NaN\n",
       "1744-09-01              13.050\n",
       "1744-10-01               8.564\n",
       "1744-11-01               4.164\n",
       "1744-12-01               0.064\n",
       "1745-01-01              -2.466\n",
       "1745-02-01              -1.641\n",
       "1745-03-01               1.968\n",
       "1745-04-01               6.816\n",
       "1745-05-01                 NaN\n",
       "1745-06-01                 NaN\n",
       "1745-07-01                 NaN\n",
       "1745-08-01                 NaN\n",
       "1745-09-01                 NaN\n",
       "1745-10-01                 NaN\n",
       "1745-11-01                 NaN\n",
       "1745-12-01                 NaN\n",
       "1746-01-01                 NaN\n",
       "1746-02-01                 NaN\n",
       "1746-03-01                 NaN\n",
       "1746-04-01                 NaN\n",
       "...                        ...\n",
       "2011-04-01              11.706\n",
       "2011-05-01              14.242\n",
       "2011-06-01              16.977\n",
       "2011-07-01              16.538\n",
       "2011-08-01              18.152\n",
       "2011-09-01              15.605\n",
       "2011-10-01               9.498\n",
       "2011-11-01               4.611\n",
       "2011-12-01               3.604\n",
       "2012-01-01               1.753\n",
       "2012-02-01              -2.467\n",
       "2012-03-01               7.115\n",
       "2012-04-01               8.259\n",
       "2012-05-01              14.474\n",
       "2012-06-01              15.623\n",
       "2012-07-01              17.834\n",
       "2012-08-01              18.822\n",
       "2012-09-01              14.030\n",
       "2012-10-01               8.847\n",
       "2012-11-01               5.220\n",
       "2012-12-01               1.216\n",
       "2013-01-01              -0.067\n",
       "2013-02-01              -0.731\n",
       "2013-03-01               0.394\n",
       "2013-04-01               8.213\n",
       "2013-05-01              12.151\n",
       "2013-06-01              15.927\n",
       "2013-07-01              19.762\n",
       "2013-08-01              18.233\n",
       "2013-09-01                 NaN\n",
       "\n",
       "[3239 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filtering data starting from 1970-01-01\n",
    "df_Germany = df_Germany.loc['1970-01-01':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>-2.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>-1.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>1.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>5.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>11.665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature\n",
       "dt                            \n",
       "1970-01-01              -2.721\n",
       "1970-02-01              -1.331\n",
       "1970-03-01               1.234\n",
       "1970-04-01               5.512\n",
       "1970-05-01              11.665"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-09-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1970-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature\n",
       "dt                            \n",
       "1970-01-01                 NaN\n",
       "1970-02-01                 NaN\n",
       "1970-03-01                 NaN\n",
       "1970-04-01                 NaN\n",
       "1970-05-01                 NaN\n",
       "1970-06-01                 NaN\n",
       "1970-07-01                 NaN\n",
       "1970-08-01                 NaN\n",
       "1970-09-01                 NaN\n",
       "1970-10-01                 NaN\n",
       "1970-11-01                 NaN\n",
       "1970-12-01                 NaN\n",
       "1971-01-01                 NaN\n",
       "1971-02-01                 NaN\n",
       "1971-03-01                 NaN\n",
       "1971-04-01                 NaN\n",
       "1971-05-01                 NaN\n",
       "1971-06-01                 NaN\n",
       "1971-07-01                 NaN\n",
       "1971-08-01                 NaN\n",
       "1971-09-01                 NaN\n",
       "1971-10-01                 NaN\n",
       "1971-11-01                 NaN\n",
       "1971-12-01                 NaN\n",
       "1972-01-01                 NaN\n",
       "1972-02-01                 NaN\n",
       "1972-03-01                 NaN\n",
       "1972-04-01                 NaN\n",
       "1972-05-01                 NaN\n",
       "1972-06-01                 NaN\n",
       "...                        ...\n",
       "2011-04-01                 NaN\n",
       "2011-05-01                 NaN\n",
       "2011-06-01                 NaN\n",
       "2011-07-01                 NaN\n",
       "2011-08-01                 NaN\n",
       "2011-09-01                 NaN\n",
       "2011-10-01                 NaN\n",
       "2011-11-01                 NaN\n",
       "2011-12-01                 NaN\n",
       "2012-01-01                 NaN\n",
       "2012-02-01                 NaN\n",
       "2012-03-01                 NaN\n",
       "2012-04-01                 NaN\n",
       "2012-05-01                 NaN\n",
       "2012-06-01                 NaN\n",
       "2012-07-01                 NaN\n",
       "2012-08-01                 NaN\n",
       "2012-09-01                 NaN\n",
       "2012-10-01                 NaN\n",
       "2012-11-01                 NaN\n",
       "2012-12-01                 NaN\n",
       "2013-01-01                 NaN\n",
       "2013-02-01                 NaN\n",
       "2013-03-01                 NaN\n",
       "2013-04-01                 NaN\n",
       "2013-05-01                 NaN\n",
       "2013-06-01                 NaN\n",
       "2013-07-01                 NaN\n",
       "2013-08-01                 NaN\n",
       "2013-09-01                 NaN\n",
       "\n",
       "[525 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing whether there are null values\n",
    "df_Germany[df_Germany.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "525"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_Germany[df_Germany.isnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_Germany = df_Germany.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1970-01-01', '1970-02-01', '1970-03-01', '1970-04-01',\n",
       "               '1970-05-01', '1970-06-01', '1970-07-01', '1970-08-01',\n",
       "               '1970-09-01', '1970-10-01',\n",
       "               ...\n",
       "               '2012-12-01', '2013-01-01', '2013-02-01', '2013-03-01',\n",
       "               '2013-04-01', '2013-05-01', '2013-06-01', '2013-07-01',\n",
       "               '2013-08-01', '2013-09-01'],\n",
       "              dtype='datetime64[ns]', name='dt', length=525, freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replacing NaN values with the previous effective data\n",
    "df_Germany.AverageTemperature.fillna(method='pad', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [AverageTemperature]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany[df_Germany.AverageTemperature.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.895307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.695429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.281000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.306000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.417000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.343000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AverageTemperature\n",
       "count          525.000000\n",
       "mean             8.895307\n",
       "std              6.695429\n",
       "min             -6.281000\n",
       "25%              3.306000\n",
       "50%              8.417000\n",
       "75%             15.011000\n",
       "max             22.343000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_Germany['Ticks'] = range(0,len(df_Germany.index.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>-2.721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>-1.331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>1.234</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>5.512</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>11.665</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>17.371</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>16.565</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>17.229</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-09-01</th>\n",
       "      <td>13.804</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>8.986</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  Ticks\n",
       "dt                                   \n",
       "1970-01-01              -2.721      0\n",
       "1970-02-01              -1.331      1\n",
       "1970-03-01               1.234      2\n",
       "1970-04-01               5.512      3\n",
       "1970-05-01              11.665      4\n",
       "1970-06-01              17.371      5\n",
       "1970-07-01              16.565      6\n",
       "1970-08-01              17.229      7\n",
       "1970-09-01              13.804      8\n",
       "1970-10-01               8.986      9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>1.216</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.067</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>-0.731</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>0.394</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>8.213</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>12.151</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>15.927</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>19.762</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>18.233</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>18.233</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  Ticks\n",
       "dt                                   \n",
       "2012-12-01               1.216    515\n",
       "2013-01-01              -0.067    516\n",
       "2013-02-01              -0.731    517\n",
       "2013-03-01               0.394    518\n",
       "2013-04-01               8.213    519\n",
       "2013-05-01              12.151    520\n",
       "2013-06-01              15.927    521\n",
       "2013-07-01              19.762    522\n",
       "2013-08-01              18.233    523\n",
       "2013-09-01              18.233    524"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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K1G3+/ubKUC5D9v1Ozrk3OOcec859lP3sR51zDznnPtz++Yr9HgeJgIyyCIGF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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#very simple plotting\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('Ticks')\n",
    "ax1.set_ylabel('Avg. Temp.')\n",
    "ax1.set_title('Original Plot')\n",
    "ax1.plot('Ticks', 'AverageTemperature', data = df_Germany);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>Ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>-2.721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>-1.331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>1.234</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>5.512</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>11.665</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>17.371</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>16.565</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>17.229</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-09-01</th>\n",
       "      <td>13.804</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>8.986</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-11-01</th>\n",
       "      <td>5.708</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-12-01</th>\n",
       "      <td>0.251</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>-1.329</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-02-01</th>\n",
       "      <td>1.290</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-03-01</th>\n",
       "      <td>1.058</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-04-01</th>\n",
       "      <td>8.441</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>14.176</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>14.359</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>18.213</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>18.424</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-09-01</th>\n",
       "      <td>12.613</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-10-01</th>\n",
       "      <td>9.220</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-11-01</th>\n",
       "      <td>3.527</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-12-01</th>\n",
       "      <td>3.210</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>-2.377</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>1.947</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>5.607</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>7.302</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>11.796</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>14.785</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>11.706</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>14.242</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>16.977</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>16.538</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>18.152</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>15.605</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>9.498</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>4.611</td>\n",
       "      <td>502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>3.604</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>1.753</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>-2.467</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>7.115</td>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>8.259</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>14.474</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>15.623</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>17.834</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>18.822</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>14.030</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>8.847</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>5.220</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>1.216</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.067</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>-0.731</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>0.394</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-01</th>\n",
       "      <td>8.213</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>12.151</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>15.927</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>19.762</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>18.233</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>18.233</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  Ticks\n",
       "dt                                   \n",
       "1970-01-01              -2.721      0\n",
       "1970-02-01              -1.331      1\n",
       "1970-03-01               1.234      2\n",
       "1970-04-01               5.512      3\n",
       "1970-05-01              11.665      4\n",
       "1970-06-01              17.371      5\n",
       "1970-07-01              16.565      6\n",
       "1970-08-01              17.229      7\n",
       "1970-09-01              13.804      8\n",
       "1970-10-01               8.986      9\n",
       "1970-11-01               5.708     10\n",
       "1970-12-01               0.251     11\n",
       "1971-01-01              -1.329     12\n",
       "1971-02-01               1.290     13\n",
       "1971-03-01               1.058     14\n",
       "1971-04-01               8.441     15\n",
       "1971-05-01              14.176     16\n",
       "1971-06-01              14.359     17\n",
       "1971-07-01              18.213     18\n",
       "1971-08-01              18.424     19\n",
       "1971-09-01              12.613     20\n",
       "1971-10-01               9.220     21\n",
       "1971-11-01               3.527     22\n",
       "1971-12-01               3.210     23\n",
       "1972-01-01              -2.377     24\n",
       "1972-02-01               1.947     25\n",
       "1972-03-01               5.607     26\n",
       "1972-04-01               7.302     27\n",
       "1972-05-01              11.796     28\n",
       "1972-06-01              14.785     29\n",
       "...                        ...    ...\n",
       "2011-04-01              11.706    495\n",
       "2011-05-01              14.242    496\n",
       "2011-06-01              16.977    497\n",
       "2011-07-01              16.538    498\n",
       "2011-08-01              18.152    499\n",
       "2011-09-01              15.605    500\n",
       "2011-10-01               9.498    501\n",
       "2011-11-01               4.611    502\n",
       "2011-12-01               3.604    503\n",
       "2012-01-01               1.753    504\n",
       "2012-02-01              -2.467    505\n",
       "2012-03-01               7.115    506\n",
       "2012-04-01               8.259    507\n",
       "2012-05-01              14.474    508\n",
       "2012-06-01              15.623    509\n",
       "2012-07-01              17.834    510\n",
       "2012-08-01              18.822    511\n",
       "2012-09-01              14.030    512\n",
       "2012-10-01               8.847    513\n",
       "2012-11-01               5.220    514\n",
       "2012-12-01               1.216    515\n",
       "2013-01-01              -0.067    516\n",
       "2013-02-01              -0.731    517\n",
       "2013-03-01               0.394    518\n",
       "2013-04-01               8.213    519\n",
       "2013-05-01              12.151    520\n",
       "2013-06-01              15.927    521\n",
       "2013-07-01              19.762    522\n",
       "2013-08-01              18.233    523\n",
       "2013-09-01              18.233    524\n",
       "\n",
       "[525 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "def stationarity_check(ts):\n",
    "    \n",
    "    # Determing rolling statistics\n",
    "    roll_mean = pd.rolling_mean(ts, window=12)\n",
    "    # Plot rolling statistics:\n",
    "    plt.plot(ts, color='green',label='Original')\n",
    "    plt.plot(roll_mean, color='blue', label='Rolling Mean')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    # Perform Augmented Dickey-Fuller test:\n",
    "    print('Augmented Dickey-Fuller test:')\n",
    "    df_test = adfuller(ts)\n",
    "    print(\"type of df_test: \",type(df_test))\n",
    "    print(\"df_test: \",df_test)\n",
    "    df_output = pd.Series(df_test[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    print(\"df_output: \\n\",df_output)\n",
    "    for key,value in df_test[4].items():\n",
    "        df_output['Critical Value (%s)'%key] = value\n",
    "    print(df_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:7: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(center=False,window=12).mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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++O8ueNf7W7/uKfaqnGLX0b6J2MOMp6rduhTDsdteEp2IJXKIPeTl3z3Ke1Df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rn/zihWxWKUHwqOihFxVEyMGMVEMRj7rVLKW4pQTgwL7lp0F8YPLiw3c6nQ46iYWIqJEaMoiKWY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\nADSlcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller test:\n",
      "type of df_test:  <class 'tuple'>\n",
      "df_test:  (-4.271198430066125, 0.00049808780753740904, 16, 508, {'1%': -3.4432882895877501, '10%': -2.5698092313534628, '5%': -2.8672462791357867}, 2060.5638228165117)\n",
      "df_output: \n",
      " Test Statistic                  -4.271198\n",
      "p-value                          0.000498\n",
      "#Lags Used                      16.000000\n",
      "Number of Observations Used    508.000000\n",
      "dtype: float64\n",
      "Test Statistic                  -4.271198\n",
      "p-value                          0.000498\n",
      "#Lags Used                      16.000000\n",
      "Number of Observations Used    508.000000\n",
      "Critical Value (1%)             -3.443288\n",
      "Critical Value (10%)            -2.569809\n",
      "Critical Value (5%)             -2.867246\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stationarity_check(df_Germany.AverageTemperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(center=False,window=12).mean()\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df_Germany['Roll_Mean'] = pd.rolling_mean(df_Germany.AverageTemperature, window=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(center=False,window=12).mean()\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.pd.rolling_mean(df_Germany.AverageTemperature, window=12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>Ticks</th>\n",
       "      <th>Roll_Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>-2.721</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>-1.331</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>1.234</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>5.512</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>11.665</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>17.371</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>16.565</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>17.229</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-09-01</th>\n",
       "      <td>13.804</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>8.986</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-11-01</th>\n",
       "      <td>5.708</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-12-01</th>\n",
       "      <td>0.251</td>\n",
       "      <td>11</td>\n",
       "      <td>7.856083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>-1.329</td>\n",
       "      <td>12</td>\n",
       "      <td>7.972083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-02-01</th>\n",
       "      <td>1.290</td>\n",
       "      <td>13</td>\n",
       "      <td>8.190500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-03-01</th>\n",
       "      <td>1.058</td>\n",
       "      <td>14</td>\n",
       "      <td>8.175833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-04-01</th>\n",
       "      <td>8.441</td>\n",
       "      <td>15</td>\n",
       "      <td>8.419917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>14.176</td>\n",
       "      <td>16</td>\n",
       "      <td>8.629167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>14.359</td>\n",
       "      <td>17</td>\n",
       "      <td>8.378167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>18.213</td>\n",
       "      <td>18</td>\n",
       "      <td>8.515500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>18.424</td>\n",
       "      <td>19</td>\n",
       "      <td>8.615083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-09-01</th>\n",
       "      <td>12.613</td>\n",
       "      <td>20</td>\n",
       "      <td>8.515833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-10-01</th>\n",
       "      <td>9.220</td>\n",
       "      <td>21</td>\n",
       "      <td>8.535333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-11-01</th>\n",
       "      <td>3.527</td>\n",
       "      <td>22</td>\n",
       "      <td>8.353583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-12-01</th>\n",
       "      <td>3.210</td>\n",
       "      <td>23</td>\n",
       "      <td>8.600167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>-2.377</td>\n",
       "      <td>24</td>\n",
       "      <td>8.512833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>1.947</td>\n",
       "      <td>25</td>\n",
       "      <td>8.567583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>5.607</td>\n",
       "      <td>26</td>\n",
       "      <td>8.946667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>7.302</td>\n",
       "      <td>27</td>\n",
       "      <td>8.851750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>11.796</td>\n",
       "      <td>28</td>\n",
       "      <td>8.653417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>14.785</td>\n",
       "      <td>29</td>\n",
       "      <td>8.688917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-07-01</th>\n",
       "      <td>18.028</td>\n",
       "      <td>30</td>\n",
       "      <td>8.673500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-08-01</th>\n",
       "      <td>16.222</td>\n",
       "      <td>31</td>\n",
       "      <td>8.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-09-01</th>\n",
       "      <td>11.278</td>\n",
       "      <td>32</td>\n",
       "      <td>8.378750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-10-01</th>\n",
       "      <td>7.088</td>\n",
       "      <td>33</td>\n",
       "      <td>8.201083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-11-01</th>\n",
       "      <td>4.281</td>\n",
       "      <td>34</td>\n",
       "      <td>8.263917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-12-01</th>\n",
       "      <td>0.701</td>\n",
       "      <td>35</td>\n",
       "      <td>8.054833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>-0.199</td>\n",
       "      <td>36</td>\n",
       "      <td>8.236333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-02-01</th>\n",
       "      <td>1.047</td>\n",
       "      <td>37</td>\n",
       "      <td>8.161333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-03-01</th>\n",
       "      <td>4.274</td>\n",
       "      <td>38</td>\n",
       "      <td>8.050250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-04-01</th>\n",
       "      <td>5.255</td>\n",
       "      <td>39</td>\n",
       "      <td>7.879667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  Ticks  Roll_Mean\n",
       "dt                                              \n",
       "1970-01-01              -2.721      0        NaN\n",
       "1970-02-01              -1.331      1        NaN\n",
       "1970-03-01               1.234      2        NaN\n",
       "1970-04-01               5.512      3        NaN\n",
       "1970-05-01              11.665      4        NaN\n",
       "1970-06-01              17.371      5        NaN\n",
       "1970-07-01              16.565      6        NaN\n",
       "1970-08-01              17.229      7        NaN\n",
       "1970-09-01              13.804      8        NaN\n",
       "1970-10-01               8.986      9        NaN\n",
       "1970-11-01               5.708     10        NaN\n",
       "1970-12-01               0.251     11   7.856083\n",
       "1971-01-01              -1.329     12   7.972083\n",
       "1971-02-01               1.290     13   8.190500\n",
       "1971-03-01               1.058     14   8.175833\n",
       "1971-04-01               8.441     15   8.419917\n",
       "1971-05-01              14.176     16   8.629167\n",
       "1971-06-01              14.359     17   8.378167\n",
       "1971-07-01              18.213     18   8.515500\n",
       "1971-08-01              18.424     19   8.615083\n",
       "1971-09-01              12.613     20   8.515833\n",
       "1971-10-01               9.220     21   8.535333\n",
       "1971-11-01               3.527     22   8.353583\n",
       "1971-12-01               3.210     23   8.600167\n",
       "1972-01-01              -2.377     24   8.512833\n",
       "1972-02-01               1.947     25   8.567583\n",
       "1972-03-01               5.607     26   8.946667\n",
       "1972-04-01               7.302     27   8.851750\n",
       "1972-05-01              11.796     28   8.653417\n",
       "1972-06-01              14.785     29   8.688917\n",
       "1972-07-01              18.028     30   8.673500\n",
       "1972-08-01              16.222     31   8.490000\n",
       "1972-09-01              11.278     32   8.378750\n",
       "1972-10-01               7.088     33   8.201083\n",
       "1972-11-01               4.281     34   8.263917\n",
       "1972-12-01               0.701     35   8.054833\n",
       "1973-01-01              -0.199     36   8.236333\n",
       "1973-02-01               1.047     37   8.161333\n",
       "1973-03-01               4.274     38   8.050250\n",
       "1973-04-01               5.255     39   7.879667"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Germany.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf,plot_acf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3hjIBZ0bjBxnlwohc6OTCSF1PInVOdz7LRTEzlixcwJKFFz6XIkiCRBBdeDls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\nsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4JXl8C3BxWiZJZwPTge9XWZ6ZmWWg2uA/PSK2AyT/jzixXVIb8AXgj4b6MElL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\nUjMVXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df_Germany.AverageTemperature, lags=50)\n",
    "plot_pacf(df_Germany.AverageTemperature, lags=50)\n",
    "plt.xlabel('lags')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(0, 0) - AIC:3489.3808038198003\n",
      "ARMA(0, 1) - AIC:3055.991615279617\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(0, 3) - AIC:2737.8610808444973\n",
      "ARMA(1, 0) - AIC:2902.0000084299527\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(1, 1) - AIC:2794.013184224455\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(1, 2) - AIC:3051.0480389761124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(1, 3) - AIC:2852.213870733137\n",
      "ARMA(2, 0) - AIC:2655.3745330345246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARMA(2, 2) - AIC:2118.4924160016612\n",
      "ARMA(2, 3) - AIC:2098.692939238398\n",
      "ARMA(3, 0) - AIC:2463.259677694068\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "p = q = range(0, 4)\n",
    "pq = itertools.product(p, q)\n",
    "for param in pq:\n",
    "    try:\n",
    "        mod = ARMA(df_Germany.AverageTemperature,order=param)\n",
    "        results = mod.fit()\n",
    "        print('ARMA{} - AIC:{}'.format(param, results.aic))\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:171: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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uxISgkj59y62DX6RP9PZS9JG0Dza06j3UbudZlvK3Tt5LydmlcAM5IYtiuJN2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RaaeNr2SQ1teu/5aDCzPSxzNtHRWqi+Q1e3ITMz3NMj9rGV76fDBMomHqF5mweMiEevbBoE1ZVC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\nxLMUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARMA(df_Germany.AverageTemperature, order=(2,3))  \n",
    "results_MA = model.fit()  \n",
    "plt.plot(df_Germany.AverageTemperature)\n",
    "plt.plot(results_MA.fittedvalues, color='red')\n",
    "plt.title('Fitting data _ MSE: %.2f'% (((results_MA.fittedvalues-df_Germany.AverageTemperature)**2).mean()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = results_MA.predict('01/01/1970', '12/01/2023')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1970-01-01     8.875399\n",
       "1970-02-01    -0.276641\n",
       "1970-03-01     2.495447\n",
       "1970-04-01     6.434733\n",
       "1970-05-01    11.341208\n",
       "1970-06-01    16.358221\n",
       "1970-07-01    19.519907\n",
       "1970-08-01    18.103874\n",
       "1970-09-01    15.548821\n",
       "1970-10-01    10.501860\n",
       "1970-11-01     5.173943\n",
       "1970-12-01     1.513140\n",
       "1971-01-01    -1.089679\n",
       "1971-02-01    -0.428033\n",
       "1971-03-01     3.171551\n",
       "1971-04-01     6.830478\n",
       "1971-05-01    12.763905\n",
       "1971-06-01    17.017426\n",
       "1971-07-01    17.977879\n",
       "1971-08-01    17.932774\n",
       "1971-09-01    15.038202\n",
       "1971-10-01     9.639163\n",
       "1971-11-01     5.001591\n",
       "1971-12-01     0.737157\n",
       "1972-01-01    -0.151104\n",
       "1972-02-01    -0.470696\n",
       "1972-03-01     3.687871\n",
       "1972-04-01     8.391127\n",
       "1972-05-01    12.613759\n",
       "1972-06-01    16.397804\n",
       "                ...    \n",
       "2021-07-01    17.996595\n",
       "2021-08-01    16.941749\n",
       "2021-09-01    13.724865\n",
       "2021-10-01     9.208222\n",
       "2021-11-01     4.602462\n",
       "2021-12-01     1.142092\n",
       "2022-01-01    -0.245415\n",
       "2022-02-01     0.811800\n",
       "2022-03-01     4.030317\n",
       "2022-04-01     8.547417\n",
       "2022-05-01    13.152336\n",
       "2022-06-01    16.610794\n",
       "2022-07-01    17.995828\n",
       "2022-08-01    16.936244\n",
       "2022-09-01    13.716097\n",
       "2022-10-01     9.198541\n",
       "2022-11-01     4.594464\n",
       "2022-12-01     1.137919\n",
       "2023-01-01    -0.244643\n",
       "2023-02-01     0.817309\n",
       "2023-03-01     4.039086\n",
       "2023-04-01     8.557097\n",
       "2023-05-01    13.160331\n",
       "2023-06-01    16.614962\n",
       "2023-07-01    17.995051\n",
       "2023-08-01    16.930731\n",
       "2023-09-01    13.707325\n",
       "2023-10-01     9.188862\n",
       "2023-11-01     4.586472\n",
       "2023-12-01     1.133756\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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